{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Regression with Continuous"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.138Z",
          "iopub.execute_input": "2022-08-18T00:38:54.148Z",
          "shell.execute_reply": "2022-08-18T00:38:53.975Z",
          "iopub.status.idle": "2022-08-18T00:38:54.158Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.169Z",
          "iopub.execute_input": "2022-08-18T00:38:54.178Z",
          "shell.execute_reply": "2022-08-18T00:38:53.985Z",
          "iopub.status.idle": "2022-08-18T00:38:54.200Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-08-18T00:38:53.996Z",
          "iopub.status.busy": "2022-08-18T00:38:54.212Z",
          "iopub.execute_input": "2022-08-18T00:38:54.220Z",
          "iopub.status.idle": "2022-08-18T00:38:54.229Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume  Increase_Decrease  \\\nDate                                                                          \n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200                  1   \n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300                  1   \n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100                  0   \n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700                  0   \n2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600                  0   \n\n            Buy_Sell_on_Open  Buy_Sell   Returns  \nDate                                              \n2014-01-03                 1         1  0.012658  \n2014-01-06                 1         1  0.032500  \n2014-01-07                 1         0  0.012106  \n2014-01-08                 0         0  0.000000  \n2014-01-09                 0         1 -0.021531  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.012106</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.20</td>\n      <td>4.23</td>\n      <td>4.05</td>\n      <td>4.09</td>\n      <td>4.09</td>\n      <td>30667600</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.021531</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-08-18T00:38:54.005Z",
          "iopub.status.busy": "2022-08-18T00:38:54.242Z",
          "iopub.execute_input": "2022-08-18T00:38:54.250Z",
          "iopub.status.idle": "2022-08-18T00:38:54.263Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "                 Open   High        Low      Close  Adj Close     Volume  \\\nDate                                                                       \n2018-08-20  19.790001  20.08  19.350000  19.980000  19.980000   62983200   \n2018-08-21  19.980000  20.42  19.860001  20.400000  20.400000   55629000   \n2018-08-22  20.280001  20.92  20.209999  20.900000  20.900000   62002700   \n2018-08-23  21.190001  22.32  21.139999  22.290001  22.290001  113444100   \n2018-08-24  22.910000  24.00  22.670000  23.980000  23.980000  164328200   \n\n            Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \nDate                                                                 \n2018-08-20                  0                 1         1  0.010622  \n2018-08-21                  1                 1         1  0.021021  \n2018-08-22                  1                 1         1  0.024510  \n2018-08-23                  1                 1         1  0.066507  \n2018-08-24                  0                 0         0  0.075819  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-08-20</th>\n      <td>19.790001</td>\n      <td>20.08</td>\n      <td>19.350000</td>\n      <td>19.980000</td>\n      <td>19.980000</td>\n      <td>62983200</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.010622</td>\n    </tr>\n    <tr>\n      <th>2018-08-21</th>\n      <td>19.980000</td>\n      <td>20.42</td>\n      <td>19.860001</td>\n      <td>20.400000</td>\n      <td>20.400000</td>\n      <td>55629000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.021021</td>\n    </tr>\n    <tr>\n      <th>2018-08-22</th>\n      <td>20.280001</td>\n      <td>20.92</td>\n      <td>20.209999</td>\n      <td>20.900000</td>\n      <td>20.900000</td>\n      <td>62002700</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.024510</td>\n    </tr>\n    <tr>\n      <th>2018-08-23</th>\n      <td>21.190001</td>\n      <td>22.32</td>\n      <td>21.139999</td>\n      <td>22.290001</td>\n      <td>22.290001</td>\n      <td>113444100</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.066507</td>\n    </tr>\n    <tr>\n      <th>2018-08-24</th>\n      <td>22.910000</td>\n      <td>24.00</td>\n      <td>22.670000</td>\n      <td>23.980000</td>\n      <td>23.980000</td>\n      <td>164328200</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.075819</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-08-18T00:38:54.016Z",
          "iopub.status.busy": "2022-08-18T00:38:54.274Z",
          "iopub.execute_input": "2022-08-18T00:38:54.282Z",
          "iopub.status.idle": "2022-08-18T00:38:54.296Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "(1170, 10)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-08-18T00:38:54.025Z",
          "iopub.status.busy": "2022-08-18T00:38:54.309Z",
          "iopub.execute_input": "2022-08-18T00:38:54.318Z",
          "iopub.status.idle": "2022-08-18T00:38:54.332Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.344Z",
          "iopub.execute_input": "2022-08-18T00:38:54.352Z",
          "iopub.status.idle": "2022-08-18T00:38:54.362Z",
          "shell.execute_reply": "2022-08-18T00:38:54.035Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target = dataset['Adj Close']\n",
        "features = dataset[['Open']]"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.376Z",
          "iopub.execute_input": "2022-08-18T00:38:54.385Z",
          "iopub.status.idle": "2022-08-18T00:38:54.395Z",
          "shell.execute_reply": "2022-08-18T00:38:54.043Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_lr = LinearRegression()"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.408Z",
          "iopub.execute_input": "2022-08-18T00:38:54.416Z",
          "iopub.status.idle": "2022-08-18T00:38:54.425Z",
          "shell.execute_reply": "2022-08-18T00:38:54.051Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_lr.fit(X=features, y=target)\n",
        "\n",
        "\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "LinearRegression()"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.437Z",
          "iopub.execute_input": "2022-08-18T00:38:54.446Z",
          "iopub.status.idle": "2022-08-18T00:38:54.459Z",
          "shell.execute_reply": "2022-08-18T00:38:54.059Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "                 Open\nDate                 \n2014-01-03   3.980000\n2014-01-06   4.010000\n2014-01-07   4.190000\n2014-01-08   4.230000\n2014-01-09   4.200000\n...               ...\n2018-08-20  19.790001\n2018-08-21  19.980000\n2018-08-22  20.280001\n2018-08-23  21.190001\n2018-08-24  22.910000\n\n[1170 rows x 1 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.980000</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.010000</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.190000</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.230000</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.200000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2018-08-20</th>\n      <td>19.790001</td>\n    </tr>\n    <tr>\n      <th>2018-08-21</th>\n      <td>19.980000</td>\n    </tr>\n    <tr>\n      <th>2018-08-22</th>\n      <td>20.280001</td>\n    </tr>\n    <tr>\n      <th>2018-08-23</th>\n      <td>21.190001</td>\n    </tr>\n    <tr>\n      <th>2018-08-24</th>\n      <td>22.910000</td>\n    </tr>\n  </tbody>\n</table>\n<p>1170 rows × 1 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.471Z",
          "iopub.execute_input": "2022-08-18T00:38:54.478Z",
          "iopub.status.idle": "2022-08-18T00:38:54.494Z",
          "shell.execute_reply": "2022-08-18T00:38:54.067Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_lr.predict(X=features)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "array([ 3.98127966,  4.01128168,  4.19129234, ..., 20.28226164,\n       21.19231627, 22.91241914])"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.508Z",
          "iopub.execute_input": "2022-08-18T00:38:54.516Z",
          "shell.execute_reply": "2022-08-18T00:38:54.075Z",
          "iopub.status.idle": "2022-08-18T00:38:54.531Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['pred_lr'] = model_lr.predict(X=features)\n",
        "dataset\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "                 Open   High        Low      Close  Adj Close     Volume  \\\nDate                                                                       \n2014-01-03   3.980000   4.00   3.880000   4.000000   4.000000   22887200   \n2014-01-06   4.010000   4.18   3.990000   4.130000   4.130000   42398300   \n2014-01-07   4.190000   4.25   4.110000   4.180000   4.180000   42932100   \n2014-01-08   4.230000   4.26   4.140000   4.180000   4.180000   30678700   \n2014-01-09   4.200000   4.23   4.050000   4.090000   4.090000   30667600   \n...               ...    ...        ...        ...        ...        ...   \n2018-08-20  19.790001  20.08  19.350000  19.980000  19.980000   62983200   \n2018-08-21  19.980000  20.42  19.860001  20.400000  20.400000   55629000   \n2018-08-22  20.280001  20.92  20.209999  20.900000  20.900000   62002700   \n2018-08-23  21.190001  22.32  21.139999  22.290001  22.290001  113444100   \n2018-08-24  22.910000  24.00  22.670000  23.980000  23.980000  164328200   \n\n            Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns    pred_lr  \nDate                                                                            \n2014-01-03                  1                 1         1  0.012658   3.981280  \n2014-01-06                  1                 1         1  0.032500   4.011282  \n2014-01-07                  0                 1         0  0.012106   4.191292  \n2014-01-08                  0                 0         0  0.000000   4.231295  \n2014-01-09                  0                 0         1 -0.021531   4.201293  \n...                       ...               ...       ...       ...        ...  \n2018-08-20                  0                 1         1  0.010622  19.792232  \n2018-08-21                  1                 1         1  0.021021  19.982242  \n2018-08-22                  1                 1         1  0.024510  20.282262  \n2018-08-23                  1                 1         1  0.066507  21.192316  \n2018-08-24                  0                 0         0  0.075819  22.912419  \n\n[1170 rows x 11 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n      <th>pred_lr</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.980000</td>\n      <td>4.00</td>\n      <td>3.880000</td>\n      <td>4.000000</td>\n      <td>4.000000</td>\n      <td>22887200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n      <td>3.981280</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.010000</td>\n      <td>4.18</td>\n      <td>3.990000</td>\n      <td>4.130000</td>\n      <td>4.130000</td>\n      <td>42398300</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n      <td>4.011282</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.190000</td>\n      <td>4.25</td>\n      <td>4.110000</td>\n      <td>4.180000</td>\n      <td>4.180000</td>\n      <td>42932100</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.012106</td>\n      <td>4.191292</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.230000</td>\n      <td>4.26</td>\n      <td>4.140000</td>\n      <td>4.180000</td>\n      <td>4.180000</td>\n      <td>30678700</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>4.231295</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.200000</td>\n      <td>4.23</td>\n      <td>4.050000</td>\n      <td>4.090000</td>\n      <td>4.090000</td>\n      <td>30667600</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.021531</td>\n      <td>4.201293</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2018-08-20</th>\n      <td>19.790001</td>\n      <td>20.08</td>\n      <td>19.350000</td>\n      <td>19.980000</td>\n      <td>19.980000</td>\n      <td>62983200</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.010622</td>\n      <td>19.792232</td>\n    </tr>\n    <tr>\n      <th>2018-08-21</th>\n      <td>19.980000</td>\n      <td>20.42</td>\n      <td>19.860001</td>\n      <td>20.400000</td>\n      <td>20.400000</td>\n      <td>55629000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.021021</td>\n      <td>19.982242</td>\n    </tr>\n    <tr>\n      <th>2018-08-22</th>\n      <td>20.280001</td>\n      <td>20.92</td>\n      <td>20.209999</td>\n      <td>20.900000</td>\n      <td>20.900000</td>\n      <td>62002700</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.024510</td>\n      <td>20.282262</td>\n    </tr>\n    <tr>\n      <th>2018-08-23</th>\n      <td>21.190001</td>\n      <td>22.32</td>\n      <td>21.139999</td>\n      <td>22.290001</td>\n      <td>22.290001</td>\n      <td>113444100</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.066507</td>\n      <td>21.192316</td>\n    </tr>\n    <tr>\n      <th>2018-08-24</th>\n      <td>22.910000</td>\n      <td>24.00</td>\n      <td>22.670000</td>\n      <td>23.980000</td>\n      <td>23.980000</td>\n      <td>164328200</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.075819</td>\n      <td>22.912419</td>\n    </tr>\n  </tbody>\n</table>\n<p>1170 rows × 11 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-08-18T00:38:54.082Z",
          "iopub.status.busy": "2022-08-18T00:38:54.543Z",
          "iopub.execute_input": "2022-08-18T00:38:54.551Z",
          "iopub.status.idle": "2022-08-18T00:38:54.567Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='Open', y='Adj Close', data=dataset)\n",
        "sns.scatterplot(x='Open', y='pred_lr', data=dataset)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='Open', ylabel='Adj Close'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.579Z",
          "iopub.execute_input": "2022-08-18T00:38:54.586Z",
          "iopub.status.idle": "2022-08-18T00:38:54.604Z",
          "shell.execute_reply": "2022-08-18T00:38:54.091Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='Open', y='Adj Close', data=dataset)\n",
        "sns.scatterplot(x='Open', y='pred_lr', data=dataset)\n",
        "sns.lineplot(x='Open', y='pred_lr', data=dataset, color='orange')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='Open', ylabel='Adj Close'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.616Z",
          "iopub.execute_input": "2022-08-18T00:38:54.624Z",
          "shell.execute_reply": "2022-08-18T00:38:55.471Z",
          "iopub.status.idle": "2022-08-18T00:38:54.647Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_lr.score(X=features, y=target)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "0.997658173314538"
          },
          "metadata": {}
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.660Z",
          "iopub.execute_input": "2022-08-18T00:38:54.667Z",
          "iopub.status.idle": "2022-08-18T00:38:54.684Z",
          "shell.execute_reply": "2022-08-18T00:38:55.477Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Adj Close'] - dataset['pred_lr']"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "Date\n2014-01-03    0.018720\n2014-01-06    0.118718\n2014-01-07   -0.011293\n2014-01-08   -0.051295\n2014-01-09   -0.111293\n                ...   \n2018-08-20    0.187767\n2018-08-21    0.417757\n2018-08-22    0.617738\n2018-08-23    1.097685\n2018-08-24    1.067580\nLength: 1170, dtype: float64"
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.697Z",
          "iopub.execute_input": "2022-08-18T00:38:54.705Z",
          "iopub.status.idle": "2022-08-18T00:38:54.721Z",
          "shell.execute_reply": "2022-08-18T00:38:55.482Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['residuals'] = dataset['Open'] - dataset['pred_lr']\n",
        "dataset"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": "                 Open   High        Low      Close  Adj Close     Volume  \\\nDate                                                                       \n2014-01-03   3.980000   4.00   3.880000   4.000000   4.000000   22887200   \n2014-01-06   4.010000   4.18   3.990000   4.130000   4.130000   42398300   \n2014-01-07   4.190000   4.25   4.110000   4.180000   4.180000   42932100   \n2014-01-08   4.230000   4.26   4.140000   4.180000   4.180000   30678700   \n2014-01-09   4.200000   4.23   4.050000   4.090000   4.090000   30667600   \n...               ...    ...        ...        ...        ...        ...   \n2018-08-20  19.790001  20.08  19.350000  19.980000  19.980000   62983200   \n2018-08-21  19.980000  20.42  19.860001  20.400000  20.400000   55629000   \n2018-08-22  20.280001  20.92  20.209999  20.900000  20.900000   62002700   \n2018-08-23  21.190001  22.32  21.139999  22.290001  22.290001  113444100   \n2018-08-24  22.910000  24.00  22.670000  23.980000  23.980000  164328200   \n\n            Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \\\nDate                                                                  \n2014-01-03                  1                 1         1  0.012658   \n2014-01-06                  1                 1         1  0.032500   \n2014-01-07                  0                 1         0  0.012106   \n2014-01-08                  0                 0         0  0.000000   \n2014-01-09                  0                 0         1 -0.021531   \n...                       ...               ...       ...       ...   \n2018-08-20                  0                 1         1  0.010622   \n2018-08-21                  1                 1         1  0.021021   \n2018-08-22                  1                 1         1  0.024510   \n2018-08-23                  1                 1         1  0.066507   \n2018-08-24                  0                 0         0  0.075819   \n\n              pred_lr  residuals  \nDate                              \n2014-01-03   3.981280  -0.001280  \n2014-01-06   4.011282  -0.001281  \n2014-01-07   4.191292  -0.001292  \n2014-01-08   4.231295  -0.001295  \n2014-01-09   4.201293  -0.001293  \n...               ...        ...  \n2018-08-20  19.792232  -0.002231  \n2018-08-21  19.982242  -0.002243  \n2018-08-22  20.282262  -0.002261  \n2018-08-23  21.192316  -0.002316  \n2018-08-24  22.912419  -0.002419  \n\n[1170 rows x 12 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n      <th>pred_lr</th>\n      <th>residuals</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.980000</td>\n      <td>4.00</td>\n      <td>3.880000</td>\n      <td>4.000000</td>\n      <td>4.000000</td>\n      <td>22887200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n      <td>3.981280</td>\n      <td>-0.001280</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.010000</td>\n      <td>4.18</td>\n      <td>3.990000</td>\n      <td>4.130000</td>\n      <td>4.130000</td>\n      <td>42398300</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n      <td>4.011282</td>\n      <td>-0.001281</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.190000</td>\n      <td>4.25</td>\n      <td>4.110000</td>\n      <td>4.180000</td>\n      <td>4.180000</td>\n      <td>42932100</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.012106</td>\n      <td>4.191292</td>\n      <td>-0.001292</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.230000</td>\n      <td>4.26</td>\n      <td>4.140000</td>\n      <td>4.180000</td>\n      <td>4.180000</td>\n      <td>30678700</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>4.231295</td>\n      <td>-0.001295</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.200000</td>\n      <td>4.23</td>\n      <td>4.050000</td>\n      <td>4.090000</td>\n      <td>4.090000</td>\n      <td>30667600</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.021531</td>\n      <td>4.201293</td>\n      <td>-0.001293</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2018-08-20</th>\n      <td>19.790001</td>\n      <td>20.08</td>\n      <td>19.350000</td>\n      <td>19.980000</td>\n      <td>19.980000</td>\n      <td>62983200</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.010622</td>\n      <td>19.792232</td>\n      <td>-0.002231</td>\n    </tr>\n    <tr>\n      <th>2018-08-21</th>\n      <td>19.980000</td>\n      <td>20.42</td>\n      <td>19.860001</td>\n      <td>20.400000</td>\n      <td>20.400000</td>\n      <td>55629000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.021021</td>\n      <td>19.982242</td>\n      <td>-0.002243</td>\n    </tr>\n    <tr>\n      <th>2018-08-22</th>\n      <td>20.280001</td>\n      <td>20.92</td>\n      <td>20.209999</td>\n      <td>20.900000</td>\n      <td>20.900000</td>\n      <td>62002700</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.024510</td>\n      <td>20.282262</td>\n      <td>-0.002261</td>\n    </tr>\n    <tr>\n      <th>2018-08-23</th>\n      <td>21.190001</td>\n      <td>22.32</td>\n      <td>21.139999</td>\n      <td>22.290001</td>\n      <td>22.290001</td>\n      <td>113444100</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.066507</td>\n      <td>21.192316</td>\n      <td>-0.002316</td>\n    </tr>\n    <tr>\n      <th>2018-08-24</th>\n      <td>22.910000</td>\n      <td>24.00</td>\n      <td>22.670000</td>\n      <td>23.980000</td>\n      <td>23.980000</td>\n      <td>164328200</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.075819</td>\n      <td>22.912419</td>\n      <td>-0.002419</td>\n    </tr>\n  </tbody>\n</table>\n<p>1170 rows × 12 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 18,
      "metadata": {
        "collapsed": true,
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          "source_hidden": false,
          "outputs_hidden": false
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        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.733Z",
          "iopub.execute_input": "2022-08-18T00:38:54.741Z",
          "iopub.status.idle": "2022-08-18T00:38:54.761Z",
          "shell.execute_reply": "2022-08-18T00:38:55.487Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.residuals.sum()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": "-1.7099978923797408"
          },
          "metadata": {}
        }
      ],
      "execution_count": 19,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
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        "nteract": {
          "transient": {
            "deleting": false
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        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.775Z",
          "iopub.execute_input": "2022-08-18T00:38:54.785Z",
          "iopub.status.idle": "2022-08-18T00:38:54.804Z",
          "shell.execute_reply": "2022-08-18T00:38:55.492Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.residuals.sum().round(2)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "-1.71"
          },
          "metadata": {}
        }
      ],
      "execution_count": 20,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.818Z",
          "iopub.execute_input": "2022-08-18T00:38:54.827Z",
          "iopub.status.idle": "2022-08-18T00:38:54.845Z",
          "shell.execute_reply": "2022-08-18T00:38:55.497Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['residuals^2'] = dataset.residuals**2\n",
        "dataset"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "                 Open   High        Low      Close  Adj Close     Volume  \\\nDate                                                                       \n2014-01-03   3.980000   4.00   3.880000   4.000000   4.000000   22887200   \n2014-01-06   4.010000   4.18   3.990000   4.130000   4.130000   42398300   \n2014-01-07   4.190000   4.25   4.110000   4.180000   4.180000   42932100   \n2014-01-08   4.230000   4.26   4.140000   4.180000   4.180000   30678700   \n2014-01-09   4.200000   4.23   4.050000   4.090000   4.090000   30667600   \n...               ...    ...        ...        ...        ...        ...   \n2018-08-20  19.790001  20.08  19.350000  19.980000  19.980000   62983200   \n2018-08-21  19.980000  20.42  19.860001  20.400000  20.400000   55629000   \n2018-08-22  20.280001  20.92  20.209999  20.900000  20.900000   62002700   \n2018-08-23  21.190001  22.32  21.139999  22.290001  22.290001  113444100   \n2018-08-24  22.910000  24.00  22.670000  23.980000  23.980000  164328200   \n\n            Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \\\nDate                                                                  \n2014-01-03                  1                 1         1  0.012658   \n2014-01-06                  1                 1         1  0.032500   \n2014-01-07                  0                 1         0  0.012106   \n2014-01-08                  0                 0         0  0.000000   \n2014-01-09                  0                 0         1 -0.021531   \n...                       ...               ...       ...       ...   \n2018-08-20                  0                 1         1  0.010622   \n2018-08-21                  1                 1         1  0.021021   \n2018-08-22                  1                 1         1  0.024510   \n2018-08-23                  1                 1         1  0.066507   \n2018-08-24                  0                 0         0  0.075819   \n\n              pred_lr  residuals  residuals^2  \nDate                                           \n2014-01-03   3.981280  -0.001280     0.000002  \n2014-01-06   4.011282  -0.001281     0.000002  \n2014-01-07   4.191292  -0.001292     0.000002  \n2014-01-08   4.231295  -0.001295     0.000002  \n2014-01-09   4.201293  -0.001293     0.000002  \n...               ...        ...          ...  \n2018-08-20  19.792232  -0.002231     0.000005  \n2018-08-21  19.982242  -0.002243     0.000005  \n2018-08-22  20.282262  -0.002261     0.000005  \n2018-08-23  21.192316  -0.002316     0.000005  \n2018-08-24  22.912419  -0.002419     0.000006  \n\n[1170 rows x 13 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n      <th>pred_lr</th>\n      <th>residuals</th>\n      <th>residuals^2</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.980000</td>\n      <td>4.00</td>\n      <td>3.880000</td>\n      <td>4.000000</td>\n      <td>4.000000</td>\n      <td>22887200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n      <td>3.981280</td>\n      <td>-0.001280</td>\n      <td>0.000002</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.010000</td>\n      <td>4.18</td>\n      <td>3.990000</td>\n      <td>4.130000</td>\n      <td>4.130000</td>\n      <td>42398300</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n      <td>4.011282</td>\n      <td>-0.001281</td>\n      <td>0.000002</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.190000</td>\n      <td>4.25</td>\n      <td>4.110000</td>\n      <td>4.180000</td>\n      <td>4.180000</td>\n      <td>42932100</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.012106</td>\n      <td>4.191292</td>\n      <td>-0.001292</td>\n      <td>0.000002</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.230000</td>\n      <td>4.26</td>\n      <td>4.140000</td>\n      <td>4.180000</td>\n      <td>4.180000</td>\n      <td>30678700</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>4.231295</td>\n      <td>-0.001295</td>\n      <td>0.000002</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.200000</td>\n      <td>4.23</td>\n      <td>4.050000</td>\n      <td>4.090000</td>\n      <td>4.090000</td>\n      <td>30667600</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.021531</td>\n      <td>4.201293</td>\n      <td>-0.001293</td>\n      <td>0.000002</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2018-08-20</th>\n      <td>19.790001</td>\n      <td>20.08</td>\n      <td>19.350000</td>\n      <td>19.980000</td>\n      <td>19.980000</td>\n      <td>62983200</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.010622</td>\n      <td>19.792232</td>\n      <td>-0.002231</td>\n      <td>0.000005</td>\n    </tr>\n    <tr>\n      <th>2018-08-21</th>\n      <td>19.980000</td>\n      <td>20.42</td>\n      <td>19.860001</td>\n      <td>20.400000</td>\n      <td>20.400000</td>\n      <td>55629000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.021021</td>\n      <td>19.982242</td>\n      <td>-0.002243</td>\n      <td>0.000005</td>\n    </tr>\n    <tr>\n      <th>2018-08-22</th>\n      <td>20.280001</td>\n      <td>20.92</td>\n      <td>20.209999</td>\n      <td>20.900000</td>\n      <td>20.900000</td>\n      <td>62002700</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.024510</td>\n      <td>20.282262</td>\n      <td>-0.002261</td>\n      <td>0.000005</td>\n    </tr>\n    <tr>\n      <th>2018-08-23</th>\n      <td>21.190001</td>\n      <td>22.32</td>\n      <td>21.139999</td>\n      <td>22.290001</td>\n      <td>22.290001</td>\n      <td>113444100</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.066507</td>\n      <td>21.192316</td>\n      <td>-0.002316</td>\n      <td>0.000005</td>\n    </tr>\n    <tr>\n      <th>2018-08-24</th>\n      <td>22.910000</td>\n      <td>24.00</td>\n      <td>22.670000</td>\n      <td>23.980000</td>\n      <td>23.980000</td>\n      <td>164328200</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.075819</td>\n      <td>22.912419</td>\n      <td>-0.002419</td>\n      <td>0.000006</td>\n    </tr>\n  </tbody>\n</table>\n<p>1170 rows × 13 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
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        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.862Z",
          "iopub.execute_input": "2022-08-18T00:38:54.871Z",
          "iopub.status.idle": "2022-08-18T00:38:54.897Z",
          "shell.execute_reply": "2022-08-18T00:38:55.501Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['residuals^2'].sum()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": "0.0025991192921877267"
          },
          "metadata": {}
        }
      ],
      "execution_count": 22,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.934Z",
          "iopub.execute_input": "2022-08-18T00:38:54.944Z",
          "iopub.status.idle": "2022-08-18T00:38:54.963Z",
          "shell.execute_reply": "2022-08-18T00:38:55.506Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "RSS = dataset['residuals^2'].sum()\n",
        "RSS"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "0.0025991192921877267"
          },
          "metadata": {}
        }
      ],
      "execution_count": 23,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-18T00:38:54.975Z",
          "iopub.execute_input": "2022-08-18T00:38:54.983Z",
          "iopub.status.idle": "2022-08-18T00:38:54.999Z",
          "shell.execute_reply": "2022-08-18T00:38:55.511Z"
        }
      }
    },
    {
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          "execution_count": 24,
          "data": {
            "text/plain": "Date\n2014-01-03     3.980000\n2014-01-06     4.010000\n2014-01-07     4.190000\n2014-01-08     4.230000\n2014-01-09     4.200000\n                ...    \n2018-08-20    19.790001\n2018-08-21    19.980000\n2018-08-22    20.280001\n2018-08-23    21.190001\n2018-08-24    22.910000\nName: Open, Length: 1170, dtype: float64"
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          "execution_count": 26,
          "data": {
            "text/plain": "Date\n2014-01-03    -3.021342\n2014-01-06    -2.991342\n2014-01-07    -2.811342\n2014-01-08    -2.771342\n2014-01-09    -2.801342\n                ...    \n2018-08-20    12.788659\n2018-08-21    12.978658\n2018-08-22    13.278659\n2018-08-23    14.188659\n2018-08-24    15.908658\nName: Open, Length: 1170, dtype: float64"
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          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": "                 Open   High        Low      Close  Adj Close     Volume  \\\nDate                                                                       \n2014-01-03   3.980000   4.00   3.880000   4.000000   4.000000   22887200   \n2014-01-06   4.010000   4.18   3.990000   4.130000   4.130000   42398300   \n2014-01-07   4.190000   4.25   4.110000   4.180000   4.180000   42932100   \n2014-01-08   4.230000   4.26   4.140000   4.180000   4.180000   30678700   \n2014-01-09   4.200000   4.23   4.050000   4.090000   4.090000   30667600   \n...               ...    ...        ...        ...        ...        ...   \n2018-08-20  19.790001  20.08  19.350000  19.980000  19.980000   62983200   \n2018-08-21  19.980000  20.42  19.860001  20.400000  20.400000   55629000   \n2018-08-22  20.280001  20.92  20.209999  20.900000  20.900000   62002700   \n2018-08-23  21.190001  22.32  21.139999  22.290001  22.290001  113444100   \n2018-08-24  22.910000  24.00  22.670000  23.980000  23.980000  164328200   \n\n            Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \\\nDate                                                                  \n2014-01-03                  1                 1         1  0.012658   \n2014-01-06                  1                 1         1  0.032500   \n2014-01-07                  0                 1         0  0.012106   \n2014-01-08                  0                 0         0  0.000000   \n2014-01-09                  0                 0         1 -0.021531   \n...                       ...               ...       ...       ...   \n2018-08-20                  0                 1         1  0.010622   \n2018-08-21                  1                 1         1  0.021021   \n2018-08-22                  1                 1         1  0.024510   \n2018-08-23                  1                 1         1  0.066507   \n2018-08-24                  0                 0         0  0.075819   \n\n              pred_lr  residuals  residuals^2  real_residuals  \nDate                                                           \n2014-01-03   3.981280  -0.001280     0.000002       -3.021342  \n2014-01-06   4.011282  -0.001281     0.000002       -2.991342  \n2014-01-07   4.191292  -0.001292     0.000002       -2.811342  \n2014-01-08   4.231295  -0.001295     0.000002       -2.771342  \n2014-01-09   4.201293  -0.001293     0.000002       -2.801342  \n...               ...        ...          ...             ...  \n2018-08-20  19.792232  -0.002231     0.000005       12.788659  \n2018-08-21  19.982242  -0.002243     0.000005       12.978658  \n2018-08-22  20.282262  -0.002261     0.000005       13.278659  \n2018-08-23  21.192316  -0.002316     0.000005       14.188659  \n2018-08-24  22.912419  -0.002419     0.000006       15.908658  \n\n[1170 rows x 14 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n      <th>pred_lr</th>\n      <th>residuals</th>\n      <th>residuals^2</th>\n      <th>real_residuals</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.980000</td>\n      <td>4.00</td>\n      <td>3.880000</td>\n      <td>4.000000</td>\n      <td>4.000000</td>\n      <td>22887200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n      <td>3.981280</td>\n      <td>-0.001280</td>\n      <td>0.000002</td>\n      <td>-3.021342</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.010000</td>\n      <td>4.18</td>\n      <td>3.990000</td>\n      <td>4.130000</td>\n      <td>4.130000</td>\n      <td>42398300</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n      <td>4.011282</td>\n      <td>-0.001281</td>\n      <td>0.000002</td>\n      <td>-2.991342</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.190000</td>\n      <td>4.25</td>\n      <td>4.110000</td>\n      <td>4.180000</td>\n      <td>4.180000</td>\n      <td>42932100</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.012106</td>\n      <td>4.191292</td>\n      <td>-0.001292</td>\n      <td>0.000002</td>\n      <td>-2.811342</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.230000</td>\n      <td>4.26</td>\n      <td>4.140000</td>\n      <td>4.180000</td>\n      <td>4.180000</td>\n      <td>30678700</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>4.231295</td>\n      <td>-0.001295</td>\n      <td>0.000002</td>\n      <td>-2.771342</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.200000</td>\n      <td>4.23</td>\n      <td>4.050000</td>\n      <td>4.090000</td>\n      <td>4.090000</td>\n      <td>30667600</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.021531</td>\n      <td>4.201293</td>\n      <td>-0.001293</td>\n      <td>0.000002</td>\n      <td>-2.801342</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2018-08-20</th>\n      <td>19.790001</td>\n      <td>20.08</td>\n      <td>19.350000</td>\n      <td>19.980000</td>\n      <td>19.980000</td>\n      <td>62983200</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.010622</td>\n      <td>19.792232</td>\n      <td>-0.002231</td>\n      <td>0.000005</td>\n      <td>12.788659</td>\n    </tr>\n    <tr>\n      <th>2018-08-21</th>\n      <td>19.980000</td>\n      <td>20.42</td>\n      <td>19.860001</td>\n      <td>20.400000</td>\n      <td>20.400000</td>\n      <td>55629000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.021021</td>\n      <td>19.982242</td>\n      <td>-0.002243</td>\n      <td>0.000005</td>\n      <td>12.978658</td>\n    </tr>\n    <tr>\n      <th>2018-08-22</th>\n      <td>20.280001</td>\n      <td>20.92</td>\n      <td>20.209999</td>\n      <td>20.900000</td>\n      <td>20.900000</td>\n      <td>62002700</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.024510</td>\n      <td>20.282262</td>\n      <td>-0.002261</td>\n      <td>0.000005</td>\n      <td>13.278659</td>\n    </tr>\n    <tr>\n      <th>2018-08-23</th>\n      <td>21.190001</td>\n      <td>22.32</td>\n      <td>21.139999</td>\n      <td>22.290001</td>\n      <td>22.290001</td>\n      <td>113444100</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.066507</td>\n      <td>21.192316</td>\n      <td>-0.002316</td>\n      <td>0.000005</td>\n      <td>14.188659</td>\n    </tr>\n    <tr>\n      <th>2018-08-24</th>\n      <td>22.910000</td>\n      <td>24.00</td>\n      <td>22.670000</td>\n      <td>23.980000</td>\n      <td>23.980000</td>\n      <td>164328200</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.075819</td>\n      <td>22.912419</td>\n      <td>-0.002419</td>\n      <td>0.000006</td>\n      <td>15.908658</td>\n    </tr>\n  </tbody>\n</table>\n<p>1170 rows × 14 columns</p>\n</div>"
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      "execution_count": 28,
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          "execution_count": 29,
          "data": {
            "text/plain": "27561.564995489553"
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      "execution_count": 29,
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        "TSS"
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          "execution_count": 30,
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          "execution_count": 31,
          "data": {
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